Methods for Analyzing the Evolution of Email Spam

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dc.contributor.advisorLowd, Daniel
dc.contributor.authorNachenahalli Bhuthegowda, Bharath Kumar
dc.date.accessioned2019-01-11T22:29:31Z
dc.date.available2019-01-11T22:29:31Z
dc.date.issued2019-01-11
dc.description.abstractEmail spam has steadily grown and has become a major problem for users, email service providers, and many other organizations. Many adversarial methods have been proposed to combat spam and various studies have been made on the evolution of email spam, by finding evolution patterns and trends based on historical spam data and by incorporating spam filters. In this thesis, we try to understand the evolution of email spam and how we can build better classifiers that will remain effective against adaptive adversaries like spammers. We compare various methods for analyzing the evolution of spam emails by incorporating spam filters along with a spam dataset. We explore the trends based on the weights of the features learned by the classifiers and the accuracies of the classifiers trained and tested in different settings. We also evaluate the effectiveness of the classifier trained in adversarial settings on synthetic data.en_US
dc.identifier.urihttps://hdl.handle.net/1794/24213
dc.language.isoen_US
dc.publisherUniversity of Oregon
dc.rightsAll Rights Reserved.
dc.subjectAdversarial classificationen_US
dc.subjectEmail spamen_US
dc.subjectEvolution of email spamen_US
dc.subjectMachine learningen_US
dc.subjectSpam detectionen_US
dc.titleMethods for Analyzing the Evolution of Email Spam
dc.typeElectronic Thesis or Dissertation
thesis.degree.disciplineDepartment of Computer and Information Science
thesis.degree.grantorUniversity of Oregon
thesis.degree.levelmasters
thesis.degree.nameM.S.

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